{"id":"W2071657723","doi":"10.1517/17460440903544456","title":"High-content screening for the discovery of pharmacological compounds: advantages, challenges and potential benefits of recent technological developments.","year":2010,"lang":"en","type":"article","venue":"PubMed","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University and Génome Québec Innovation Centre","funders":"","keywords":"Drug discovery; Data science; Biochemical engineering; High-content screening; Nanotechnology; Risk analysis (engineering); Computer science; Chemistry; Business; Engineering; Materials science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004124173,0.0001138331,0.0001927369,0.00003980141,0.00004623358,0.00001151764,0.0002376648,0.000139478,0.000002618902],"category_scores_gemma":[0.000171009,0.0000745459,0.0000765396,0.00004385225,0.000264718,0.000007342449,0.0002434224,0.0001094383,4.207304e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004094666,"about_ca_system_score_gemma":0.00000885221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005270612,"about_ca_topic_score_gemma":0.00001826562,"domain_scores_codex":[0.999173,0.00002411761,0.0002430756,0.0002557865,0.0001089748,0.0001950035],"domain_scores_gemma":[0.9994695,0.00004491534,0.0001468476,0.0001937108,0.0001117225,0.00003330495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001275671,0.0000939622,0.0003814526,0.0000106827,0.0001804241,5.128924e-7,0.000004967325,0.0000031952,0.5826049,0.0003241505,0.00007549229,0.4161927],"study_design_scores_gemma":[0.0004813829,0.00006526407,0.1179509,0.000002996952,0.000115982,0.000006830292,0.00007008165,0.00000802132,0.8756064,0.0000819204,0.005507415,0.0001028837],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904384,0.004045604,0.003956182,0.0006309292,0.0000486307,0.0007269667,0.00001641709,0.00001917064,0.0001177259],"genre_scores_gemma":[0.9809726,0.01343527,0.004883918,0.00005455947,0.00005221553,0.0005132714,0.00003610523,0.00000894995,0.00004308208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4160898,"threshold_uncertainty_score":0.3039894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04412945456407349,"score_gpt":0.2658614725586849,"score_spread":0.2217320179946114,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}